RVC/Retrieval-based-Voice-Conversion-WebUI
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768VecOnnxExport (#328)

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* Delete export_onnx.py

* Delete export_onnx_old.py

* Delete models_onnx_moess.py

* Support 768 Vec

* Add files via upload

* Support 768 Vec

Support 768 Vec

* Support 768 Vec Onnx Export

Support 768 Vec Onnx Export
This commit is contained in:
Ναρουσέ·μ·γιουμεμί·Χινακάννα
2023-05-21 19:11:29 +08:00
committed by GitHub
parent c3de24f2e0
commit 067731db9b
5 changed files with 144 additions and 1046 deletions
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102
infer-web.py
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@@ -1068,15 +1068,11 @@ def change_info_(ckpt_path):
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
from infer_pack.models_onnx_moess import SynthesizerTrnMs256NSFsidM
from infer_pack.models_onnx import SynthesizerTrnMs256NSFsidO
from infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
def export_onnx(ModelPath, ExportedPath, MoeVS=True):
hidden_channels = 256 # hidden_channels，为768Vec做准备
cpt = torch.load(ModelPath, map_location="cpu")
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
print(*cpt["config"])
hidden_channels = cpt["config"][-2] # hidden_channels，为768Vec做准备
test_phone = torch.rand(1, 200, hidden_channels) # hidden unit
test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用）
@@ -1087,68 +1083,38 @@ def export_onnx(ModelPath, ExportedPath, MoeVS=True):
device = "cpu" # 导出时设备（不影响使用模型）
if MoeVS:
net_g = SynthesizerTrnMs256NSFsidM(
*cpt["config"], is_half=False
) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
net_g.load_state_dict(cpt["weight"], strict=False)
input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
output_names = [
"audio",
]
torch.onnx.export(
net_g,
(
test_phone.to(device),
test_phone_lengths.to(device),
test_pitch.to(device),
test_pitchf.to(device),
test_ds.to(device),
test_rnd.to(device),
),
ExportedPath,
dynamic_axes={
"phone": [1],
"pitch": [1],
"pitchf": [1],
"rnd": [2],
},
do_constant_folding=False,
opset_version=16,
verbose=False,
input_names=input_names,
output_names=output_names,
)
else:
net_g = SynthesizerTrnMs256NSFsidO(
*cpt["config"], is_half=False
) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
net_g.load_state_dict(cpt["weight"], strict=False)
input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds"]
output_names = [
"audio",
]
torch.onnx.export(
net_g,
(
test_phone.to(device),
test_phone_lengths.to(device),
test_pitch.to(device),
test_pitchf.to(device),
test_ds.to(device),
),
ExportedPath,
dynamic_axes={
"phone": [1],
"pitch": [1],
"pitchf": [1],
},
do_constant_folding=False,
opset_version=16,
verbose=False,
input_names=input_names,
output_names=output_names,
)
net_g = SynthesizerTrnMsNSFsidM(
*cpt["config"], is_half=False
) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
net_g.load_state_dict(cpt["weight"], strict=False)
input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
output_names = [
"audio",
]
# net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出
torch.onnx.export(
net_g,
(
test_phone.to(device),
test_phone_lengths.to(device),
test_pitch.to(device),
test_pitchf.to(device),
test_ds.to(device),
test_rnd.to(device),
),
ExportedPath,
dynamic_axes={
"phone": [1],
"pitch": [1],
"pitchf": [1],
"rnd": [2],
},
do_constant_folding=False,
opset_version=16,
verbose=False,
input_names=input_names,
output_names=output_names,
)
return "Finished"